All you need to know about Emakina’s chatbot Hackathon (video)

Posted by Thomas Halter

From the 20th to the 22nd of October, 5 teams of ambitious young people gathered at Emakina in Brussels with one single objective in mind: to create the most helpful chatbot they could think of, within 48 hours. Helped by Emakinian volunteers, they dedicated their weekend to this challenging task. All 5 teams had great ideas, but the jury had to choose, and three teams managed to propose fully functional prototypes, which earned them the podium.

All participants and coaches had a great time and the atmosphere was excellent, as everyone did their best and collaborated efficiently. The jury was composed by Microsoft DX Director Erik Kerkhofs, Emakina CEO/CVO Brice Le Blévennec and ULB’s AI experts Stefan Langerman and Gianluca Bontempi. They were quite impressed by the quality of the projects and the motivation of all involved.

1st place: ENIGMOO
How would you like to create your own quest game or quizz using only Facebook Messenger? Well of course you’d love that! Having barely left their designated room during 48 hours (for human maintenance purposes only), the ENIGMOO team delivered a very neat project. Using Python for the development of the backend and Luis for the language recognition, the ENIGMOO team climbed to the top of the podium.

2nd place: WATCH YOUR EATING
This Skype-based chatbot uses an API that keeps track of what you eat (calories, carbohydrates, proteins, lipids) and offers recommendations for your next meal, in order to follow a balanced, healthy diet.

3rd place: LINGUINI
Linguini is a assistant cook: tell him what’s in your fridge and its API allows it to suggest illustrated recipes based on the ingredients you have handy. An additional feature that was not developed by the team would have given vocal instructions for the recipe, answering your questions through voice recognition.

Two projects didn’t make it to the podium: one was a personal stylist suggesting clothes that match your look (established through a visual multiple choice survey), and the other was a personal trainer suggesting exercises, based on the user’s BMI (Body Mass Index) and the objectives set, offering information about nearby gyms and sports e-shops.